TY - GEN
T1 - A Bayesian approach to Top-Scoring Pairs classification
AU - Arslan, Emre
AU - Braga-Neto, Ulisses M.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - We extend the popular Top Scoring Pair (TSP) classification rule to a Bayesian setting, with the purpose of obtaining robust and effective classifiers for small-sample, high-dimensional data. We employ the Bradley-Terry model for rank data, and infer its parameters using a previously proposed Gibbs sampling algorithm. The parameters are then used to define a Bayesian TSP score, which is used to select the gene pairs to define the proposed Bayesian TSP classifiers. Accuracy of the proposed Bayesian classification rules is evaluated against those of the conventional TSP classifiers as well as other well-known machine learning methods, using a total of 12 gene-expression data sets. The results indicate that the Bayesian k-TSP classifier obtained the best overall average accuracy rate and the best accuracy rate over the majority of the individual data sets.
AB - We extend the popular Top Scoring Pair (TSP) classification rule to a Bayesian setting, with the purpose of obtaining robust and effective classifiers for small-sample, high-dimensional data. We employ the Bradley-Terry model for rank data, and infer its parameters using a previously proposed Gibbs sampling algorithm. The parameters are then used to define a Bayesian TSP score, which is used to select the gene pairs to define the proposed Bayesian TSP classifiers. Accuracy of the proposed Bayesian classification rules is evaluated against those of the conventional TSP classifiers as well as other well-known machine learning methods, using a total of 12 gene-expression data sets. The results indicate that the Bayesian k-TSP classifier obtained the best overall average accuracy rate and the best accuracy rate over the majority of the individual data sets.
KW - Bayesian methods
KW - Gene expression classification
KW - Top Scoring Classifier
UR - http://www.scopus.com/inward/record.url?scp=85023763609&partnerID=8YFLogxK
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U2 - 10.1109/ICASSP.2017.7952280
DO - 10.1109/ICASSP.2017.7952280
M3 - Conference contribution
AN - SCOPUS:85023763609
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 871
EP - 875
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -